In the field of path planning, authors focus on global, local, and behavioral planning. The global planning algorithms provide an optimal racing line for the whole race track. This racing line is the fastest trajectory for the vehicle that needs to be followed when there are no opponents around the car. Under specific optimization objectives like minimum curvature [1]}, [2]}, minimum time [3]}, [4]} and minimum energy [5]} there are a variety of solutions to this problem. Local planning aims to achieve a high planning horizon for recursive feasibility while avoiding opponent vehicles with evasive maneuvers at high speeds. There are three main approaches for planning a local trajectory on the race track. Firstly, the global plan can be adjusted and modified via an additional optimization [6]}, [7]}. Secondly, multiple dynamically feasible trajectories are sampled. Based on racing specific cost functions, the best trajectory that avoids obstacles is selected [8]}, [9]}. Thirdly, sampling-based methods provide an efficient but non-optimal technique to randomly sample the free space around obstacles and find a possible trajectory [10]}, [11]}. Finally, the work in the field of behavioral planning covers the task of planning the behavior of the car under high uncertainty and defining interactions with non-cooperative agents. This type of behavioral planning for race cars is done either by designing multiple cost functions with weighting and then selecting the trajectory with the lowest overall cost [12]}, [13]} or by combining the local planner with game-theory methods [14]}, [15]}. Especially the ladder one showed the possibility of advanced cutting and blocking maneuvers [16]} which is crucial for the race car to succeed on the race track.
